def test_dnn_eval():
banner("dnn cv")
mnist_conf = MNIST_CONF.copy()
mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS
run_DNN(mnist_conf)
mnist_conf["init_dnn"] = {
"filename": "temp/dnn/final.nnet",
"num_hidden_layers": -1,
"with_final": 1
}
# per-part
eval_DNN(mnist_conf)
mnist_conf["eval_dnn"] = {"mode": "cv", "batch_size": 1024}
eval_DNN(mnist_conf)
mnist_conf["eval_dnn"] = {"mode": "per-feat", "batch_size": 1024}
eval_DNN(mnist_conf)
def test_dnn_eval():
banner("dnn cv")
mnist_conf = MNIST_CONF.copy()
mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS
run_DNN(mnist_conf)
mnist_conf["init_dnn"] = {
"filename": "temp/dnn/final.nnet",
"num_hidden_layers": -1,
"with_final": 1
}
# per-part
eval_DNN(mnist_conf)
mnist_conf["eval_dnn"] = {"mode": "cv", "batch_size": 1024}
eval_DNN(mnist_conf)
mnist_conf["eval_dnn"] = {"mode": "per-feat", "batch_size": 1024}
eval_DNN(mnist_conf)
def test_sda_dnn():
banner("sda dnn")
mnist_conf = MNIST_CONF.copy()
mnist_conf["train_sda"]["max_iters"] = MAX_ITERS
run_SDA(mnist_conf)
mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS
mnist_conf["init_dnn"] = {
"filename": "temp/sda/final.nnet",
"num_hidden_layers": -1,
"with_final": 1
}
run_DNN(mnist_conf)
mnist_conf["init_sda"] = {
"filename": "temp/dnn/final.nnet",
"num_hidden_layers": -1,
"with_final": 1
}
mnist_conf["train_sda"]["max_iters"] = 1
run_SDA(mnist_conf)
def test_rbm_dnn():
banner("rbm dnn")
mnist_conf = MNIST_CONF.copy()
mnist_conf["train_rbm"]["max_iters"] = MAX_ITERS
run_RBM(mnist_conf)
mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS
mnist_conf["init_dnn"] = {
"filename": "temp/rbm/final.nnet",
"num_hidden_layers": -1,
"with_final": 1
}
run_DNN(mnist_conf)
mnist_conf["init_rbm"] = {
"filename": "temp/dnn/final.nnet",
"num_hidden_layers": -1,
"with_final": 1
}
mnist_conf["train_rbm"]["max_iters"] = 0
run_RBM(mnist_conf)
def test_sda_dnn():
banner("sda dnn")
mnist_conf = MNIST_CONF.copy()
mnist_conf["train_sda"]["max_iters"] = MAX_ITERS
run_SDA(mnist_conf)
mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS
mnist_conf["init_dnn"] = {
"filename": "temp/sda/final.nnet",
"num_hidden_layers": -1,
"with_final": 1
}
run_DNN(mnist_conf)
mnist_conf["init_sda"] = {
"filename": "temp/dnn/final.nnet",
"num_hidden_layers": -1,
"with_final": 1
}
mnist_conf["train_sda"]["max_iters"] = 1
run_SDA(mnist_conf)
def test_rbm_dnn():
banner("rbm dnn")
mnist_conf = MNIST_CONF.copy()
mnist_conf["train_rbm"]["max_iters"] = MAX_ITERS
run_RBM(mnist_conf)
mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS
mnist_conf["init_dnn"] = {
"filename": "temp/rbm/final.nnet",
"num_hidden_layers": -1,
"with_final": 1
}
run_DNN(mnist_conf)
mnist_conf["init_rbm"] = {
"filename": "temp/dnn/final.nnet",
"num_hidden_layers": -1,
"with_final": 1
}
mnist_conf["train_rbm"]["max_iters"] = 0
run_RBM(mnist_conf)
def test_dropout():
banner("dropout")
mnist_conf = MNIST_CONF.copy()
mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS
mnist_conf["model"]["dropout_factor"] = "0.4"
run_DNN(mnist_conf)
def test_dropout():
banner("dropout")
mnist_conf = MNIST_CONF.copy()
mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS
mnist_conf["model"]["dropout_factor"] = "0.4"
run_DNN(mnist_conf)
